Waste Wise: Method Madness, Pt. Two

May 23, 2012

Recently EPA released its 2010 numbers, which showed that recycling increased by 5.4 percent compared to 2009. This was the single largest annual increase in recycling in a decade and the fourth largest jump in recycling since the agency began tracking this information in 1960. From 2000 to 2010, recycling increased by 12 million tons (from 53 to 65 million tons), a 22 percent rise, while the mass of discards going to landfills in that period decreased by 7.5 million tons, a drop of almost 3 percent.

Given these figures, it should come as no surprise that the number of material recovery facilities (MRFs) has more than doubled in the past 15 years in response to increased recycling efforts and demand for recycled materials. This makes recycling the fastest growing sector within the industry compared to landfilling, composting and waste-to-energy (WTE).

There has been significant interest in tracking and measuring recycling rates in a way that allows municipalities and recycling companies to maximize recycling participation, since more recyclables equals more revenue. In last month’s column I touched on using EPA’s recommended methodology to assess recycling rates. This survey-based methodology was developed in the 1990s and is still one of the most formally documented procedures for assessing recycling rates. However, as I articulated, this methodology is beset by limitations.

Additionally, while recycling skyrocketed in the last decade, waste generation remained flat, which means that growth in the recycling sector is not due to an overall increase in waste volumes, but rather from competition with other disposal methods (i. e., landfills, composting and WTE). With waste generation expected to remain flat or even decrease in the foreseeable future, increasing recycling participation is one of the only ways to grow recyclables volumes.

To increase participation and validate revenue projections, accurate recycling data must be collected at a scale that facilitates informed decisions. To get more detailed information, some companies have begun collecting route-based data by equipping truck drivers with counters, which allow the set-out rate (e. g., how many recycling bins are placed at the curb divided by the total number of recycling bins that could be set out) to be computed for a single truck route. While this most certainly is an improvement, the approach relies on the driver to remember to advance the counter manually and is time-consuming since much of the data must be compiled by hand. Any potential distraction or complication on the route could result in the driver forgetting to advance the counter.

Additionally, it is a challenge to correlate the data collected to a particular geo-spatial area such as a specific neighborhood or street. Sure, we know roughly where the truck went based on an assigned route, but if the participation rate was 50 percent, where did those cans get picked up relative to the ones that didn’t? Are there reasons why one particular neighborhood set out fewer cans than another? These questions are more difficult without knowing when and where the cans were at the time of pick-up.

Radio frequency identification technology (RFID) represents a next generation advancement in recycling. Despite some challenges stemming from perceived but largely imagined privacy issues, RFID provides some of the most detailed and least subjective data available since it requires minimal driver interaction. The collection of RFID-based data can easily be correlated with GIS data to provide very specific geo-spatial characterization. Additionally, data can also be compiled alongside demographic information such as the number of people in a household, income, age brackets and housing type.

Why do this? Research has suggested that recycling rates are largely related to human behavior, which in turn correlates to basic demographics. Thus, coupling recycling data to demographics creates a decision-making tool that can be used to isolate specific areas within a city that aren’t maximizing their rates. The collection of route-based data via counters or, preferably, RFID, allows cities to know what’s happening on a neighborhood-byneighborhood basis. As a result, decisions can be made to enhance recycling specifically in those areas with depressed recycling rates, which is much more useful and cost-effective than a broad, citywide recycling rate computed via a survey.